Autonomous collision avoidance decision-making of ships based on adaptive aggregation of optical visual information
The navigation environment of ships is complex and ever-changing,and the fusion process of multi-source visual information collected by multimodal visual cameras is complicated.The feature extraction is not precise enough.Therefore,a ship autonomous collision avoidance decision-making method based on adaptive aggregation of optical visual information is studied.Use Kinect camera to captuire multi-source images and enhance environmental perception through ad-aptive aggregation.Combining Mask RCNN and ResNet-101,accurately extract target features from image information and generate candidate regions through RPN.The feature vector enters the FCN and classification regression branches,identifies the target and locates it,and obtains the motion situation after coordinate transformation.Based on collision risk calculation and ship maneuvering experience,intelligently output collision avoidance commands.The experimental results show that this method exhibits good collision avoidance decision-making performance in complex navigation environments.After the ag-gregation of optical visual information,the intersection to union ratio and F1 score are 92.7％and 94.2％,respectively.
万方数据
维普
